Master Restb.ai with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Contact Restb.ai to request a demo or proof of concept, confirm current pricing, validate supported image formats and API limits, and test the platform against representative property photos before production integration.
💡 Quick Start: Follow these 1 steps in order to get up and running with Restb.ai quickly.
Explore the key features that make Restb.ai powerful for real estate workflows.
Detects room types, interior/exterior features, architectural styles, views, condition, quality, and damage from property photos — outputting in RESO-standardized formats.
An MLS platform auto-populates listing fields (hardwood floors, granite countertops, stainless appliances, pool) from uploaded photos, reducing agent data entry time from 15 minutes to under 2 minutes per listing.
AI-assessed condition and quality grades for each property based on visual analysis, providing standardized scores that eliminate subjective human variation.
An appraisal management company uses condition scores to pre-screen properties, flag discrepancies between agent-reported condition and photo evidence, and reduce revision requests by 50%.
Purpose-built API released January 2026 to help appraisers meet the upcoming UAD 3.6 mandate — automatically extracts required property features from photos in the new standardized format.
An appraisal firm integrates the Feature UAD API ahead of the 2026 mandate deadline, automatically populating UAD 3.6-compliant feature fields from inspection photos and reducing compliance risk.
Computer vision-enhanced comparable property matching that considers visual features, condition, and quality — not just square footage and location — for more accurate comps.
A lender's AVM adjusts its valuation model by comparing the subject property's visual condition and finishes to the most visually similar recent sales, improving accuracy for renovated homes that traditional comps undervalue.
Automatically flags property images and videos that violate MLS guidelines, website policies, or appraisal report requirements — including watermarks, blurry photos, and non-property images.
A regional MLS with 50,000 new listings monthly automatically screens every uploaded photo, rejecting images with agent watermarks, business cards, or duplicate photos before they go live.
Auto-generates descriptive alt-text for images (boosting SEO and ADA compliance) and full property descriptions from photo analysis alone.
A real estate portal auto-generates unique alt tags for 2 million listing photos, improving search engine image indexing and meeting ADA accessibility requirements without manual effort.
Restb.ai is purpose-built for real estate imagery and property workflows. Instead of only returning generic image labels, it is positioned to identify real estate-specific visual signals such as rooms, features, condition, quality, and potential damage for use in listing, valuation, compliance, and search workflows.
Restb.ai does not publish self-serve prices in the provided content. It uses paid, quote-based commercial pricing based on use case, product scope, photo or property volume, API requirements, and business terms. Buyers should contact Restb.ai for current plan names, exact prices, included limits, billing periods, overage rates, and conversion details.
The provided content confirms Restb.ai analyzes real estate photos through an API-driven workflow, but it does not provide a verified public list of supported file formats, latency targets, or technical limits. Implementation teams should confirm accepted formats and performance requirements during evaluation.
The provided content does not show a public self-serve trial. Prospective customers should contact Restb.ai to request a demo, proof of concept, or evaluation using their own property imagery.
Restb.ai says it serves more than 100 companies across the real estate ecosystem. Relevant users include MLSs, appraisal organizations, valuation providers, lenders, insurers, portals, and property search platforms.
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Tutorial updated March 2026